Mexican sign language segmentation using color based neuronal networks to detect the individual skin color

[Display omitted] •New method to detect and translate manual gestures of MLS•The Skin color detection proposed prevents poor segmentation due to bright changes.•The presented vision-based sign language detection avoid the use of extra devices like sensors.•Geometric features can represent the form of the hand shape during the gestures.•Using some representative frames of the sign we reduce the data-set. In recent years, the development of algorithms that assist in communicate with deaf people is an important challenge. The development of automatic systems to translate sign language is a current research topic. However, this involves several processes that range from video capture, pre-processing to identification or classification of the signal. The development of systems capable of extracting discriminative features that enhance the power of generalization of a classifier is even a very challenging problem. The meaning of a sign is the combination of the hand movement, hand shape, and the point of contact of the hand in the body. This paper presents a method to detect and translate hand gestures. First, we obtain 15 frames per word, obtaining 3 regions of interest (hands and face) from which we obtain geometric features. Finally we use several classifier techniques and present the experimental results.